In reality, the top end AMD 7970 has ~32 compute units, while ~$130 AMD 7770 has ~10 compute units. Each compute unit can has 4 SIMD units, each SIMD unit executes 16 single-point floating operations per clock, from a pool of 16 "hyperthread-like" work-items that are queued up. (Total of 64 FLOPs per clock)
But... that is a bit difficult to say, now isn't it :-p
In comparison, Parallela's $99 unit has 16 discrete compute units, and their (unmade) higher-end offering will have 64 compute units. Both of which will operate with 2W of power.
Its a difference of scale. GPUs focus more on SIMD instructions, because graphics are innately matrix-based operations that very easily translate into SIMD. But if you are going to be doing unique operations per core (ie: lots and lots of branching on a single work-item), then Paralella's approach would give better performance.
So... yes... I over-simplified earlier, and got a few numbers wrong. But the concept is the same. Epiphany has no SIMD units at all, and each core instead focuses entirely on single-threaded performance. (at best, 2 Floating Point operations per clock with the super-scalar architecture). This makes it slower in the strictest sense... but "more agile", and able to handle branches and conditionals better than the GPUs.
Also, remember that Epiphany works with only 2W of power... while a GPU like the AMD 7970 works with something like 300W of power. So of course, the 7970 is going to do more (especially with faster GDDR5 RAM and a PCIe x16 connection feeding it). So it isn't really a fair comparison there either.
Nonetheless, Epiphany does see a few situations where it might perform better than a classic CPU and better than a classic GPU. Its just a different software architecture, focusing on a different problem niche.
Nonetheless, Epiphany does see a few situations where it might perform better than a classic CPU and better than a classic GPU. Its just a different software architecture, focusing on a different problem niche.
By my understanding any situation where you need the computing power with the requirement for very low energy input and waste energy output.
I can't think of any specific examples (perhaps an embedded system that needs to perform a pile of cryptographic operations?) but its ability to do a significant amount of processing on the 2W power footprint it is in a different class to current desktop CPUs and GPUs. Even if this $99 unit only does 1/8 of what a $99 GPU can do (caveat: I pulled that "1/8" figure from my arse) it'll be doing it on 1/60 of the power (based on GPU reviews where people have tried to compare power draw with GPU idle to power draw with GPU at 100% but everything else as idle as possible, which indicate modern GPUs pull between 120 and 150W (http://www.guru3d.com/articles_pages/radeon_hd_6850_6870_rev... is the first such analysis Google found)). That potential computation-per-watt (or computation-per-energy-$ if the cost is more important than the energy supply+dissipation problem) of units like this could be very useful. Of course where power input and heat dissipation are not massive concerns, current GPUs still win on computation-per-device and computation-per-hardware-$.
I think the interesting comparison will be between something like Tegra 5/Exynos 5 and this. Most scientific computing these days is CUDA, and getting something like cuBLAS onto this chip will be a struggle. The Exynos/Android combo has already lowered it's ability to interest me by not even accepting OpenCL as a first class citizen.
EDIT: Hopefully NVidia will give me CUDAndroid, so I have fun.
just guessing, maybe all the cases where you need parallel computing AND low power: computer vision, robotics (drones, self driving cars etc..), automation and so on?
In reality, the top end AMD 7970 has ~32 compute units, while ~$130 AMD 7770 has ~10 compute units. Each compute unit can has 4 SIMD units, each SIMD unit executes 16 single-point floating operations per clock, from a pool of 16 "hyperthread-like" work-items that are queued up. (Total of 64 FLOPs per clock)
But... that is a bit difficult to say, now isn't it :-p
In comparison, Parallela's $99 unit has 16 discrete compute units, and their (unmade) higher-end offering will have 64 compute units. Both of which will operate with 2W of power.
Its a difference of scale. GPUs focus more on SIMD instructions, because graphics are innately matrix-based operations that very easily translate into SIMD. But if you are going to be doing unique operations per core (ie: lots and lots of branching on a single work-item), then Paralella's approach would give better performance.
So... yes... I over-simplified earlier, and got a few numbers wrong. But the concept is the same. Epiphany has no SIMD units at all, and each core instead focuses entirely on single-threaded performance. (at best, 2 Floating Point operations per clock with the super-scalar architecture). This makes it slower in the strictest sense... but "more agile", and able to handle branches and conditionals better than the GPUs.
Also, remember that Epiphany works with only 2W of power... while a GPU like the AMD 7970 works with something like 300W of power. So of course, the 7970 is going to do more (especially with faster GDDR5 RAM and a PCIe x16 connection feeding it). So it isn't really a fair comparison there either.
Nonetheless, Epiphany does see a few situations where it might perform better than a classic CPU and better than a classic GPU. Its just a different software architecture, focusing on a different problem niche.